Modern classifiers use techniques which are highly complex when high accuracy classification is needed. For example, a traditional neural network structure needing high accuracy also needs a complex structure to perform classification because of difficulty in grouping different classes within the neural network structure.
Additionally, in speech recognition systems, when a spoken command is identified, the spoken command is identified as one of a group of commands represented by a collection of command models. Many existing speech recognition systems require large amounts of processing and storage resources to identify the spoken command from the collection of command models.
A problem with existing systems is that polynomial classifiers require large amounts of processing and data storage resources to produce modest classification success. Additionally, training systems for polynomial classifiers for existing systems do not group classes using a low complexity method (e.g., training a model to represent a group of classes instead of training for each individual class model).
Another problem with existing systems is the difficulty of using low complexity classifiers to classify an unidentified class as a member of a subgroup of classes. Existing low complexity classifiers use models which represent individual classes instead of a model which represents a subgroup of classes.
Thus, what is needed is, a system and method requiring less processing and data storage resources to produce improved classification of an unidentified class (e.g., spoken command, communication channel, etc.). What is also needed is a system and method wherein unidentified classes are easily classified as a member of a subgroup of classes. What is further needed is a system and method having low complexity classifiers to classify an unidentified class as a member of a subgroup of classes and, when needed, further classifying the unidentified class within the subgroup.